As an alternative to the standard meta-learning framework, many recent papers [1]}, [2]}, [3]} study the performance of standard end-to-end pre-trained classifiers on few-shot tasks. Given minimal modification, these classifiers are actually competitive with or even outperform episodic meta-training methods. Therefore some recent works [4]}, [5]}, [6]} take advantage of both, and utilize meta-learning after pre-training, further boosting performance.
